US20200085666A1 - Walking assistance method and apparatus - Google Patents
Walking assistance method and apparatus Download PDFInfo
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- US20200085666A1 US20200085666A1 US16/412,867 US201916412867A US2020085666A1 US 20200085666 A1 US20200085666 A1 US 20200085666A1 US 201916412867 A US201916412867 A US 201916412867A US 2020085666 A1 US2020085666 A1 US 2020085666A1
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- gait phase
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Definitions
- At least one example embodiment relates to a method and/or apparatus for assisting walking of a user.
- at least some example embodiments relate to a method and/or apparatus for providing an assistance force to assist walking when the user walks.
- motion assistance apparatuses enabling the elderly and/or patients having joint problems to walk with less effort. Further, motion assistance apparatuses increasing muscular strength of users for military purposes are being developed.
- Some example embodiments relate to a walking assistance method.
- the walking assistance method includes predicting a gait phase of a user within a gait cycle based on information received from a sensor, the information being related to a motion of the user wearing a walking assistance apparatus; and controlling an assistance torque applied to the walking assistance apparatus based on the gait phase.
- the method further includes receiving, from the sensor, the information related to the motion of the user wearing the walking assistance apparatus, and wherein the gait phase includes information indicating a gait progress in a period corresponding to the gait cycle.
- the predicting includes predicting data indicative of the gait phase by inputting the information into a trained neural network, the data being encoded to have a continuity on a boundary of the gait cycle; and determining the gait phase by decoding the data.
- the method further includes encoding the data such that the data includes information indicating a vertex corresponding to the gait phase, among vertices on a circumference of a circle corresponding to the gait cycle, through a trigonometrical function.
- the method further includes encoding the data such that the data includes information indicating a vertex corresponding to the gait phase, among a plurality of vertices included in a perimeter of a figure corresponding to the gait cycle.
- the controlling includes determining the assistance torque based on the gait phase; and controlling a driver to output the assistance torque.
- the determining the assistance torque includes determining a time to apply the assistance torque based on the gait phase; and determining a magnitude of the assistance torque based on the time.
- the senor is on a foot or a shank of the user.
- the senor includes an inertial measurement unit (IMU), and wherein the receiving includes receiving, from the IMU, one or more of acceleration information and rotation velocity information.
- IMU inertial measurement unit
- the motion of the user includes one or more of a level walking motion, a level running motion, a slope walking motion, and a slope running motion of the user.
- Some example embodiments relate to a method of training a gait phase regression module (PRM).
- PRM gait phase regression module
- the method includes receiving, from a first sensor, first information related to a motion of a user wearing a walking assistance apparatus; receiving, from a second sensor, second information related to the motion of the user; determining an initial gait phase within a gait cycle based on the first information and the second information; predicting a gait phase within the gait cycle by applying the first information to the gait PRM; and training the gait PRM based on the gait phase and the initial gait phase.
- the gait phase includes information indicating a gait progress in a period corresponding to the gait cycle.
- the predicting includes predicting data indicative of the gait phase by inputting the first information into the gait PRM, the data being encoded to have a continuity on a boundary of the gait cycle; and determining the gait phase by decoding the data.
- the method further includes encoding the data such that the data includes information indicating a vertex corresponding to the gait phase, among a plurality of vertices included in a perimeter of a figure corresponding to the gait cycle.
- Some example embodiments relate to a non-transitory computer-readable medium including computer readable instructions that, when executed by a computer, cause the computer to perform a walking assistance method.
- Some example embodiments relate to a walking assistance apparatus.
- the walking assistance apparatus includes a sensor configured to measure a motion of a user wearing the walking assistance apparatus; a driver configured to assist walking of the user; and at least one processor configured to, predict a gait phase within a gait cycle of the user based on information, and control an assistance torque applied to the walking assistance apparatus based on the gait phase.
- the gait phase includes information indicative of a gait progress in a period corresponding to the gait cycle.
- the processor is configured to, predict data indicating the gait phase by inputting the information into a trained neural network, the data being encoded to have a continuity on a boundary of the gait cycle, and determine the gait phase by decoding the data.
- the processor is configured to encode the data such that the data includes information indicating a vertex corresponding to the gait phase, among a plurality of vertices included in a perimeter of a figure corresponding to the gait cycle.
- the senor includes an inertial measurement unit (IMU), the IMU being configured to measure one or more of acceleration information and rotation velocity information.
- IMU inertial measurement unit
- FIG. 1 illustrates gait states according to at least one example embodiment
- FIG. 2 illustrates a transition between gait states according to at least one example embodiment
- FIG. 3 illustrates a walking assistance apparatus according to at least one example embodiment
- FIG. 4 illustrates a walking assistance apparatus according to at least one example embodiment
- FIG. 5 illustrates a configuration of a walking assistance apparatus according to at least one example embodiment
- FIG. 6 is a flowchart illustrating a walking assistance method according to at least one example embodiment
- FIG. 7A illustrates graphs to describe forms of gait phases according to at least one example embodiment
- FIG. 7B illustrates output data of a neural network according to at least one example embodiment
- FIG. 8 illustrates a walking assistance apparatus and parameters according to at least one example embodiment
- FIG. 9 is a graph illustrating an assistance torque predetermined based on a gait phase according to at least one example embodiment
- FIG. 10 is a flowchart illustrating an example of controlling a driver by adjusting a length of a support frame according to at least one example embodiment
- FIG. 11 illustrates a walking assistance method which distinguishes between two states of stance and swing according to at least one example embodiment
- FIG. 12 is a block diagram illustrating a walking assistance system according to at least one example embodiment
- FIGS. 13 and 14 illustrate a hip-type walking assistance apparatus according to at least one example embodiment
- FIGS. 15 through 17 illustrate a full body-type walking assistance apparatus according to at least one example embodiment.
- first, second, A, B, (a), (b), and the like may be used herein to describe components. Each of these terminologies is not used to define an essence, order or sequence of a corresponding component but used merely to distinguish the corresponding component from other component(s).
- a first component may be referred to a second component, and similarly the second component may also be referred to as the first component.
- a third component may be “connected,” “coupled,” and “joined” between the first and second components, although the first component may be directly connected, coupled or joined to the second component.
- a third component may not be present therebetween.
- expressions, for example, “between” and “immediately between” and “adjacent to” and “immediately adjacent to” may also be construed as described in the foregoing.
- FIG. 1 illustrates gait states according to at least one example embodiment.
- Gait phases of a leg of a user with respect to a gait may be predefined.
- gait phases may include discontinuous values classified into a stance and a swing.
- Gait phases of a left leg may include a left stance LSt and a left swing LSw.
- Gait phases of a right leg may include a right stance RSt and a right swing RSw.
- the gait phases may have continuous values with respect to a gait progress from a predetermined start point to a predetermined end point.
- a gait phase with respect to a stance may be a gait phase based on a stance.
- a gait phase 0% with respect to the stance may be mapped to a point in time at which the stance is started
- a gait phase 60% with respect to the stance may be mapped to a point in time at which a swing is started
- a gait phase 100% with respect to the stance may be mapped to a point in time immediately before the stance is started.
- a gait phase with respect to a swing may be a gait phase based on a swing.
- a gait phase 0% with respect to the swing may be mapped to a point in time at which the swing is started, a gait phase 60% with respect to the swing may be mapped to a point in time at which a stance is started, and a gait phase 100% with respect to the swing may be mapped to a point in time immediately before the swing is started.
- the gait phases may be continuous values with respect to a gait progress with a start point of “0” and an end point of “1”.
- the stance and the swing may further be divided into a plurality of phases.
- the stance may further be divided into an initial contact, a weight bearing, a middle stance, a terminal stance, and a pre-swing.
- the swing may further be divided into an initial swing, a middle swing, and a terminal swing.
- the stance and the swing may further be divided differently depending on an example embodiment, and are not limited thereto.
- FIG. 2 illustrates a transition between gait states according to at least one example embodiment.
- gait phases of a leg may include a stance and a swing, and the stance and the swing may be performed alternately for a gait.
- a right gait state 210 with respect to a motion 200 of a right leg in response to a gait may include a right stance and a right swing.
- a stance may include a weight bearing, a middle stance, and a terminal stance, but are not limited to the disclosed and illustrated embodiments.
- a left gait state 220 with respect to a motion (not shown) of a left leg may include a left stance and a left swing.
- a normal transition between gait states may differ depending on a gait state at a time of starting a gait. Based on an order in which events indicating starts of gait states occur, the gait states may transition in an order of a right stance, a left swing, a left stance, and a right swing. After the right swing is performed, the right stance may be performed again.
- a user may feel discomfort in walking when a muscular strength of an ankle of the user is weakened due to aging or disease. For example, an end portion of a foot needs to be lifted when a leg starts swinging. If not, the swinging leg may be bumped into a floor. That is, an angle of the ankle should be adjusted as a gait phase proceeds or changes.
- a walking assistance apparatus may be provided to a user who has a difficulty in adjusting an angle of an ankle by himself or herself due to a weakened muscular strength of the ankle.
- the walking assistance apparatus may be worn around the ankle of the user, determine a gait phase of the user, and output an assistance torque corresponding to the determined gait phase.
- the assistance torque may be used to adjust the angle of the ankle of the user.
- a future gait phase as well as a gait phase of a current point in time may need to be predicted. For example, there may occur a case in which the walking assistance apparatus needs to output an assistance torque at a point in time earlier than a point in time of switching stance/swing.
- FIG. 3 illustrates a walking assistance apparatus according to at least one example embodiment.
- a walking assistance apparatus 300 may include a sole frame 310 , a lower fastener 320 , an upper fastener 330 , a first support frame 340 , a second support frame 350 , and an inertial measurement unit (IMU) 360 .
- the walking assistance apparatus 300 may predict a gait phase only using the IMU 360 , without using other types of sensors. For example, the walking assistance apparatus 300 may predict the gait phase based on data obtained only using the IMU 360 , without using a pressure sensor disposed on a sole.
- the IMU 360 may include accelerometers and gyroscopes.
- the IMU 360 may sense accelerations in a three-dimensional (3D) space using the accelerometers.
- the IMU 360 may sense rotation velocities in the 3D space using the gyroscopes.
- the IMU 360 may be positioned at a foot or a shank of a user.
- the first support frame 340 may connect the lower fastener 320 and the upper fastener 330 .
- the lower fastener 320 may be connected to the sole frame 310 .
- the second support frame 350 may connect the sole frame 310 and the upper fastener 330 .
- the upper fastener 330 may be worn on a calf or the shank of the user.
- a length of the first support frame 340 and a length of the second support frame 350 may be adjusted.
- the length of the first support frame 340 and the length of the second support frame 350 may be adjusted by a driver (not shown).
- the driver may adjust the length of the first support frame 340 and the length of the second support frame 350 using a mechanism.
- the ankle of the user may be lifted. Conversely, when the length of the first support frame 340 increases and the length of the second support frame 350 decreases, the ankle of the user may extend.
- FIG. 3 illustrates the walking assistance apparatus 300 including the first support frame 340 and the second support frame 350
- the number of support frames is not limited thereto.
- the walking assistance apparatus 300 may include only the first support frame 340 , or may include at least three support frames.
- FIG. 4 illustrates a walking assistance apparatus according to at least one example embodiment.
- a walking assistance apparatus 400 may include a sole frame 410 , a lower fastener 420 , an upper fastener 430 , a motor 440 , and an IMU 450 .
- the IMU 450 may include accelerometers and gyroscopes.
- the IMU 450 may sense accelerations in a 3D space using the accelerometers.
- the IMU 450 may sense rotation velocities in the 3D space using the gyroscopes.
- the IMU 450 may be positioned at a foot or a shank of a user.
- the motor 440 may be connected to the lower fastener 420 and the upper fastener 430 .
- a driver (not shown) may control the motor 440 to output a torque.
- an angle between the lower fastener 420 and the upper fastener 430 may be adjusted. For example, when the angle between the lower fastener 420 and the upper fastener 430 decreases, an ankle of the user may be lifted. Conversely, when the angle between the lower fastener 420 and the upper fastener 430 increases, the ankle of the user may extend.
- FIG. 5 illustrates a configuration of a walking assistance apparatus according to at least one example embodiment.
- a walking assistance apparatus 500 may include at least one sensor 510 , a processor 530 , and a driver 550 .
- the walking assistance apparatus 500 may further include a communicator 520 , and a memory 540 .
- the walking assistance apparatus 500 may correspond to the walking assistance apparatus 300 of FIG. 3 and the walking assistance apparatus 400 of FIG. 4 such that the walking assistance apparatus 500 also include the structural elements (e.g., fasteners, frames or motor) illustrated in FIGS. 3 or FIG. 4 .
- the walking assistance apparatus 500 may be an ankle exoskeleton device.
- the at least one sensor 510 may include an IMU.
- the IMU may measure accelerations and rotation velocities generated in response to a motion of the IMU.
- the IMU may measure X-axial, Y-axial, and Z-axial accelerations and X-axial, Y-axial, and Z-axial angular velocities corresponding to a gait motion of a user.
- the communicator 520 may be connected to the sensor 510 , the processor 530 , and the memory 540 , and transmit and receive data to and from the sensor 510 , the processor 530 , and the memory 540 .
- the communicator 520 may be connected to an external device and transmit and receive data to and from the external device.
- to transmit and receive “A” may be to transmit and receive “information or data indicating A”.
- the communicator 520 may be implemented by a circuitry in the walking assistance apparatus 500 .
- the communicator 520 may include an internal bus and an external bus.
- the communicator 520 may be an element which connects the walking assistance apparatus 500 and the external device.
- the communicator 520 may be an interface.
- the communicator 520 may receive data from the external device and transmit the data to the processor 530 and the memory 540 .
- the processor 530 may process the data received by the communicator 520 and data stored in the memory 540 .
- the “processor” may be a data processing device implemented by hardware including a circuit having a physical structure to perform desired operations.
- the desired operations may include instructions or codes included in a program.
- the hardware-implemented data processing device may include a microprocessor, a central processing unit (CPU), a processor core, a multi-core processor, a multiprocessor, an application-specific integrated circuit (ASIC), and a field programmable gate array (FPGA).
- the processor 530 may execute computer-readable codes (for example, software) stored in a memory, for example, the memory 540 , and instructions triggered by the processor 530 .
- computer-readable codes for example, software
- the processor 530 may execute the computer-readable codes such that the processor 530 is transformed into a special purpose processor to perform the walking assistance method of FIG. 6 and/or train a gait phase regression module (PRM) based on a predicted gait phase and a determined gait phase. Therefore, the processor 530 may improve the functioning of the walking assistance apparatus itself by increasing the accuracy of predicting continuous motions of the gait associated with a boundary of a gait cycle.
- PRM gait phase regression module
- the memory 540 may store the data received by the communicator 520 and the data processed by the processor 530 .
- the memory 540 may store the program.
- the stored program may be a set of syntaxes coded to assist walking of a user and executable by the processor 530 .
- the memory 540 may include at least one volatile memory, non-volatile memory, random access memory (RAM), flash memory, hard disk drive, and optical disk drive.
- RAM random access memory
- flash memory volatile memory
- hard disk drive volatile memory
- optical disk drive optical disk drive
- the memory 540 may store a command set, for example, software, to operate the walking assistance apparatus 500 .
- the command set to operate the walking assistance apparatus 500 may be executed by the processor 530 .
- the driver 550 may include mechanisms to adjust an angle of an ankle of the user.
- the driver 550 may include a motor, and a torque output by the motor may be used to adjust the angle of the ankle.
- the driver 550 may include a power converting device which adjusts a length of a support frame. The power converting device may convert a rotational motion generated by the driver 550 into a rectilinear motion.
- the walking assistance apparatus 500 may include a powered orthosis or robotic orthosis which assists a physiological function like an ankle exoskeleton device, and a robotic prosthesis which replaces a missing body part.
- the sensor 510 the communicator 520 , the processor 530 , the memory 540 , and the driver 550 will be described further with reference to FIGS. 6 through 17 .
- FIG. 6 is a flowchart illustrating a walking assistance method according to at least one example embodiment.
- Operations 610 through 630 may be performed by the walking assistance apparatus 500 of FIG. 5 .
- the walking assistance apparatus 500 may be implemented by one or more hardware modules, one or more software modules, or various combinations thereof.
- the walking assistance apparatus 500 may receive or obtain information related to a motion of a user from a sensor.
- the sensor 510 may include an IMU.
- the IMU may measure acceleration information, rotation velocity information, or a combination thereof in relation to a motion of a user.
- the rotation velocity information may be information related to an angular velocity.
- the walking assistance apparatus 500 may predict a gait phase of the user based on the received information related to the motion of the user.
- the gait phase may include information indicating a gait process in a predefined period corresponding to a gait cycle.
- the gait cycle may be a period including repetitive patterns occurring during walking and include, for example, the right stance RSt and the right swing RSw of FIG. 1 .
- the gait cycle may be defined separately for a right leg and a left leg. Referring to FIG. 2 , a gait cycle for the right leg and a gait cycle for the left leg may differ from each other.
- the predefined period may be a period defined repeatedly for a gait, and may include, for example, a period from a start point of a stance period to a start point of a subsequent stance period, or a period from a start point of a swing period to a start point of a subsequent swing period.
- the predefined period may include a period from a predetermined point in time instead of a start point to a corresponding point in time of a subsequent cycle.
- the walking assistance apparatus 500 may predict a gait progress in a gait cycle based on motion information measured by the sensor.
- An example of predicting a gait phase of a user will be described in detail with reference to FIGS. 7A through 8 .
- the walking assistance apparatus 500 may control an assistance torque based on the predicted gait phase. For example, the walking assistance apparatus 500 may calculate an assistance torque corresponding to the gait progress. A trajectory of the assistance torque with respect to the gait phase may be preset corresponding to the gait progress. The walking assistance apparatus 500 may determine a point in time at which the assistance torque is to be applied based on the predicted gait phase, and determine a magnitude of the assistance torque corresponding to the determined point in time.
- the walking assistance apparatus 500 may further perform operation 640 .
- the walking assistance apparatus 500 may control the driver 550 to output the assistance torque.
- An example of controlling the driver will be described further with reference to FIG. 10 .
- FIG. 7A illustrates graphs to describe forms of gait phases according to at least one example embodiment.
- a gait phase related to motion information of a user may be represented separately based on a stance and a swing.
- a graph 710 , 720 may be a graph showing discontinuous data classified into a stance and a swing.
- a stance state may be represented with a value of “1”
- a swing state may be represented with a value of “0”.
- a swing state may be represented with a value of “1”
- a stance state may be represented with a value of “0”.
- the gait phase may be represented with continuous data related to a gait progress in a predefined period corresponding to a gait cycle. For example, referring to a graph 730 , the gait phase may be mapped to “0” at a first point in time at which a stance is started, and the gait phase may be mapped to “1” at a second point in time at which the stance is restarted. The gait phase between the first point in time and the second point in time may be mapped to continuous values between “0” and “1” related to the gait progress. Referring to a graph 740 , the gait phase with respect to the swing may be mapped in a manner substantially the same as the manner described through the graph 730 .
- the graph 730 , 740 is a continuous gait phase graph, and the graph 710 , 720 is a discontinuous gait phase graph.
- a more precise control may be enabled than when the graph 710 , 720 is used.
- only two states of “0” and “1” may be determined according to the graph 710 , 720
- a point in time corresponding to a value between “0” and “1”, for example, “0.6” of the gait phase may be determined according to the graph 730 , 740 .
- the walking assistance apparatus 500 may predict continuous gait phases, for example, the graph 730 , 740 , corresponding to information related to the motion of the user, based on the information related to the motion of the user received from the sensor.
- the walking assistance apparatus 500 may use a gait phase regression module (PRM) to predict the continuous gait phases corresponding to the information related to the motion of the user.
- the gait PRM may include a pre-trained neural network.
- the walking assistance apparatus 500 may input the information related to the motion of the user into the pre-trained neural network.
- the neural network may include an input layer, a hidden layer, and an output layer.
- the input layer may have a structure suitable for receiving the information related to the motion of the user collected by the sensor, and the output layer may have a structure suitable for outputting data indicating the continuous gait phases.
- the walking assistance apparatus 500 may receive, from a first sensor, first information related to a motion of a user wearing the walking assistance apparatus.
- the first sensor may include an IMU.
- the walking assistance apparatus 500 may receive, from a second sensor, second information related to the motion of the user.
- the second sensor may include a contact sensor.
- the contact sensor may sense whether a foot of the user contacts the ground.
- the walking assistance apparatus 500 may determine a gait phase corresponding to the first information based on the second information.
- the determined gait phase may be utilized as a label or a ground truth to train the neural network.
- the walking assistance apparatus 500 may predict the gait phase by applying the first information to the gait PRM.
- the walking assistance apparatus 500 may train the gait PRM based on the predicted gait phase and the determined gait phase.
- the walking assistance apparatus 500 may train parameters of the neural network so as to minimize a difference between the predicted gait phase and the determined gait phase.
- An operation of training a neural network with respect to a gait phase may be performed by a separate server device.
- the server device may use training data provided in advance or training data collected from at least one user.
- the server device may also use training data generated by a simulation.
- the neural network may be pre-trained to predict a gait phase corresponding to acceleration information and rotation velocity information according to various gait patterns.
- the neural network may be pre-trained with a gait phase corresponding to acceleration information and rotation velocity information according to various gait patterns such as level walking, level running, slope walking, and slop running of a normal user.
- the neural network may be pre-trained with a gait phase corresponding to acceleration information and rotation velocity information according to various gait patterns such as level walking, level running, slope walking, and slop running of a user having a difficulty in normal walking.
- the neural network may be trained to predict gait phases, for example, the graph 730 , 740 , having continuous values corresponding to a gait progress.
- a smoothed result like a graph 750 may be predicted.
- a gait of a user includes continuous motions having a gait cycle
- the gait phases of the graph 730 , 740 are discontinuous on a boundary of the gait cycle.
- the neural network may not accurately predict the continuous motions of the gait on the boundary of the gait cycle.
- a precise control may be relatively difficult when compared to an example of an unsmoothed graph like the graph 730 , 740 .
- a method of training a neural network to obtain an unsmoothed graph like the graph 730 , 740 will be described further below.
- FIG. 7B illustrates output data of a neural network according to at least one example embodiment.
- a gait phase 760 having continuous values in a gait cycle may be encoded into a circle 770 having a continuity on a boundary of the gait cycle to train a neural network.
- a first gait phase 761 belonging to a second half of the gait cycle may be mapped to a vertex 771 on the circle 770
- a second gait phase 762 belonging to the boundary of the gait cycle may be mapped to a vertex 772 on the circle 770
- a third gait phase 763 belonging to a first half of a subsequent gait cycle may be mapped to a vertex 773 on the circle 770 .
- the neural network may be pre-trained to output a two-dimensional (2D) vector of cosine and sine values indicating a gait phase with respect to a stance, a 2D vector of cosine and sine values indicating a gait phase with respect to a swing, or a four-dimensional (4D) vector combining the same.
- the 2D vector may correspond to coordinates indicating a vertex on the circle 770 .
- the neural network may be trained to output data encoded to have a continuity on the boundary of the gait cycle, rather than being trained to output the gait phase 760 directly. Thus, a result predicted by the neural network may be inhibited (or, alternatively, prevented) from being smoothed on the boundary of the gait cycle.
- a smoothed gait phase may be predicted on the boundary of the gait cycle since a gait phase at a point in time immediately before a stance is started and a gait phase at a point in time at which the stance is started are discontinuous.
- a value output at a point in time immediately before the stance is started and a value output at a point in time at which the stance is started are maintained to be continuous by a periodicity of a trigonometrical function, whereby prediction of a smoothed gait phase on the boundary of the gait cycle may be inhibited (or, alternatively, prevented).
- the walking assistance apparatus 500 may obtain the gait phase by decoding the corresponding data. Operations of the inference process will be described later with reference to FIGS. 8 through 11 .
- the other figure may include a polygon, and an untwisted closed curve.
- the data may include information indicating a vertex corresponding to a gait phase, among a plurality of vertices included in a perimeter of a figure corresponding to a gait cycle.
- the data may have a continuity on the boundary of the gait cycle. For example, on a boundary between a current gait cycle to a subsequent gait cycle, a figure, for example, a polygon, may be determined such that data may change by a minimum unit representing a gait phase.
- FIG. 8 illustrates a walking assistance apparatus and parameters according to at least one example embodiment.
- an IMU 810 may be attached to a foot or a shank of a user to measure a motion of the user and obtain 6D information related to accelerations and angular velocities.
- the IMU 810 may measure X-axial, Y-axial, and Z-axial accelerations, and X-axial, Y-axial, and Z-axial angular velocities corresponding to a gait motion of the user.
- the 6D information related to the measured accelerations and angular velocities may be input into a gait phase regression module (PRM) 820 .
- the gait PRM 820 may be a module trained by machine learning, and may output a trigonometrical function value of a gait phase with respect to a stance and a gait phase with respect to a swing. Trigonometrical function data with respect to the gait phase may be referred to as first data.
- the machine learning may include a recurrent neural network (RNN).
- the trigonometrical function may include a cosine function and a sine function.
- the gait PRM 820 may output cosine values and sine values of the gait phase with respect to the stance and the gait phase with respect to the swing as a 4D vector.
- the gait PRM 820 may determine the gait phase with respect to the stance and the gait phase with respect to the swing using the 4D vector corresponding to the cosine values and the sine values of the gait phase with respect to the stance and the gait phase with respect to the swing. For example, the gait PRM 820 may obtain the gait phase with respect to the stance and the gait phase with respect to the swing by performing an arctan operation on the determined cosine and sine values.
- the gait phase obtained by the gait PRM 820 may be an unsmoothed graph like the graph 730 , 740 .
- a controller 830 may control a driver 840 to output an assistance force to assist walking of a user.
- a predetermined assistance torque may be controlled based on a gait phase at a desired point in time.
- the controller 830 may output a control signal to control the driver 840 to generate the torque.
- the controller 830 may include a communicator, a processor, and a memory.
- the driver 840 may generate the torque based on the control signal output from the controller 830 .
- the driver 840 may provide a driving force to an ankle of the user.
- the driver 840 may control the ankle such that a center of gravity of the user may be formed at a front side of a sole.
- the driver 840 may include a motor which generates a rotational torque.
- FIG. 9 is a graph illustrating an assistance torque predetermined based on a gait phase according to at least one example embodiment.
- an assistance torque needed by a user may be predetermined based on a gait phase.
- the walking assistance apparatus 500 may calculate an assistance torque corresponding to a stance or a swing.
- a point in time at which the assistance torque is to be applied and a magnitude of the assistance torque corresponding to the point in time may be determined based on the predicted gait phase.
- a trajectory of the assistance torque with respect to the gait phase may be preset.
- the trajectory of the assistance torque may be preset as shown in a graph 900 .
- an axis x may denote the point in time at which the assistance torque is to be applied
- an axis y may denote the magnitude of the assistance torque corresponding to the point in time.
- An angle of the ankle of the user may be adjusted by the assistance torque.
- the walking assistance apparatus 500 may control a driver to output an assistance torque. An example of controlling the driver will be described in detail with reference to FIG. 10 .
- FIG. 10 is a flowchart illustrating an example of controlling a driver by adjusting a length of a support frame according to at least one example embodiment.
- Operation 640 of FIG. 6 may include operations 1010 and 1020 .
- An embodiment using operations 1010 and 1020 may correspond to the walking assistance apparatus 300 of FIG. 3 .
- the walking assistance apparatus 500 may calculate a length of a support frame corresponding to the determined assistance torque.
- the length of the support frame corresponding to the assistance torque may be stored in advance.
- the walking assistance apparatus 500 may control the driver 550 such that the support frame may have the calculated length.
- the driver 550 may adjust the length of the support frame using a power converting device.
- the power converting device may be a device which converts a rotational motion of a motor into a rectilinear motion.
- FIG. 11 illustrates a walking assistance method which distinguishes between two states of stance and swing according to at least one example embodiment.
- the classifier may output a probability of a stance P_stance and a probability of a swing P_swing.
- a walking assistance apparatus may switch an inner state as expressed by Equation 1.
- FIG. 12 is a block diagram illustrating a walking assistance system according to at least one example embodiment.
- a walking assistance system 1200 also referred to as a gait assist system or a walking assist system, may include the walking assistance apparatus 500 , and a remote controller 1210 .
- the remote controller 1210 may control an overall operation of the walking assistance apparatus 500 in response to a user input. For example, the remote controller 1210 may initiate or terminate the operation of the walking assistance apparatus 500 . Further, the remote controller 1210 may control an assistance torque output from the walking assistance apparatus 500 to control a walking assistance for the user.
- the remote controller 1210 may include a display 1230 .
- the display 1230 may be implemented by a touch screen, a liquid crystal display (LCD), a thin film transistor-LCD (TFT-LCD), a light emitting diode (LED) display, an organic LED (OLED) display, an active matrix OLED (AMOLED) display, or a flexible display.
- LCD liquid crystal display
- TFT-LCD thin film transistor-LCD
- LED light emitting diode
- OLED organic LED
- AMOLED active matrix OLED
- the remote controller 1210 may provide a user with a menu and/or a user interface (UI) corresponding to a function to control the walking assistance apparatus 500 through the display 1230 .
- UI user interface
- the display 1230 may display an operating state of the walking assistance apparatus 500 to the user based on the control of the remote controller 1210 .
- the hip-type walking assistance apparatus may be an apparatus which provides a walking assistance force to a hip joint of a user.
- the walking assistance apparatus 500 may be connected to the hip-type walking assistance apparatus via wired communication or wireless communication.
- the walking assistance apparatus 500 and the hip-type walking assistance apparatus may provide the user with an assistance torque with respect to a gait phase determined for a motion of the user.
- the walking assistance apparatus 500 may provide the assistance torque to an ankle joint of the user
- the hip-type walking assistance apparatus may provide the assistance torque to the hip joint of the user.
- FIGS. 13 and 14 illustrate a hip-type walking assistance apparatus according to at least one example embodiment.
- a hip-type walking assistance apparatus 1300 may be attached to a user and assist walking of the user.
- the walking assistance apparatus 1300 may be a wearable device.
- Example embodiments described with reference to FIGS. 13 and 14 may be applied to the hip-type walking assistance apparatus. However, the example embodiments are not limited thereto, and may be applied to any device which assists walking of a user.
- the hip-type walking assistance apparatus 1300 may include a driver 1310 , a sensor 1320 , an IMU 1330 , and a controller 1340 .
- the driver 1310 may provide a driving force to a hip joint of the user.
- the driver 1310 may be positioned on a right hip and/or a left hip of the user.
- the driver 1310 may include a motor which generates a rotational torque.
- the sensor 1320 may measure an angle of the hip joint of the user during walking.
- Information related to the angle of the hip joint sensed by the sensor 1320 may include an angle of a right hip joint, an angle of a left hip joint, a difference between the angles of both hip joints, and movement directions of the hip joints.
- the sensor 1320 may be positioned in the driver 1310 .
- the sensor 1320 may include a potentiometer.
- the potentiometer may sense right (R)-axial and left (L)-axial joint angles and R-axial and L-axial joint angular velocities corresponding to a gait motion of the user.
- the IMU 1330 may measure acceleration information and pose information during walking. For example, the IMU 1330 may sense X-axial, Y-axial, and Z-axial accelerations and X-axial, Y-axial, and Z-axial angular velocities corresponding to the gait motion of the user.
- the hip-type walking assistance apparatus 1300 may detect a point at which a foot of the user lands based on the acceleration information measured by the IMU 1330 .
- the hip-type walking assistance apparatus 1300 may include other sensors which sense a change in biosignal or quantity of motion of the user in response to the gait motion, in addition to the sensor 1320 and the IMU 1330 described above.
- the other sensors may include, for example, an electromyogram (EMG) sensor and an electroencephalogram (EEG) sensor.
- EMG electromyogram
- EEG electroencephalogram
- the controller 1340 may control the driver 1310 to output an assistance force to assist walking of the user.
- the hip-type walking assistance apparatus 1300 may include two drivers 1310 for the left hip and the right hip.
- the controller 1340 may output a control signal to control the driver 1310 to generate a torque.
- the controller 1340 may include a communicator, a processor, and a memory.
- the driver 1310 may generate the torque based on the control signal output from the controller 1340 .
- the hip-type walking assistance apparatus 1300 may include the driver 1310 for the right leg and the driver 1310 for the left leg.
- the controller 1340 may be designed to control one of the drivers 1310 .
- the controller 1340 may be designed to control all the drivers 1310 .
- the walking assistance apparatus 500 may be included in a full body-type walking assistance apparatus 1 which will be described with reference to FIGS. 15 through 17 .
- the full body-type walking assistance apparatus 1 may be a device which provides a walking assistance force to each of a hip joint, a knee joint, and an ankle joint of a user.
- FIGS. 15 through 17 illustrate a full body-type walking assistance apparatus according to at least one example embodiment.
- FIG. 15 is a front view of the full body-type walking assistance apparatus 1
- FIG. 16 is a side view of the full body-type walking assistance apparatus 1
- FIG. 17 is a rear view of the full body-type walking assistance apparatus 1 .
- the full body-type walking assistance apparatus 1 may include the driver 1310 , the sensor 1320 , the IMU 1330 , and the controller 1340 described above.
- the full body-type walking assistance apparatus 1 may have an exoskeleton structure such that the full body-type walking assistance apparatus 1 may be worn on a left leg and a right leg of a user.
- the user may perform motions such as an extension, a flexion, an adduction, and an abduction while wearing the walking assistance apparatus 1 .
- the extension may be a motion to extend a joint
- the flexion may be a motion to flex the joint.
- the adduction may be a motion to move a leg close to a central axis of a body.
- the abduction may be a motion to stretch the leg away from the central axis of the body.
- the full body-type walking assistance apparatus 1 may include a main body 10 , and mechanisms 20 , 30 , and 40 .
- the main body 10 may include a housing 11 .
- Various components may be provided in the housing 11 .
- the components provided in the housing 11 may include, for example, a CPU, a printed circuit board (PCB), various types of storage devices, and a power source.
- the main body 10 may include the controller 1340 described above.
- the controller 1340 may include a CPU and a PCB.
- the CPU may be a microprocessor.
- the microprocessor may include an arithmetic logical operator, a register, a program counter, a command decoder, and/or a control circuit on a silicon chip.
- the CPU may select a control mode suitable for a gait environment and generate a control signal to control operations of the mechanisms 20 , 30 , and 40 based on the selected control mode.
- the PCB may be a board on which a predetermined circuit is printed.
- a CPU and/or various storage devices may be provided on the PCB.
- the PCB may be fixed to an inner side surface of the housing 11 .
- the storage devices may be magnetic disk storage devices to store data by magnetizing a surface of a magnetic disk, and semiconductor memory devices to store data using various types of memory semiconductors.
- the power source provided in the housing 11 may supply a driving power to the various components provided in the housing 11 , or the mechanisms 20 , 30 , and 40 .
- the main body 10 may further include a waist support 12 to support a waist of the user.
- the waist support 12 may have a shape of a curved plane so as to support the waist of the user.
- the main body 10 may further include a fixer 11 a to fix the housing 11 to a hip of the user, and a fixer 12 a to fix the waist support 12 to the waist of the user.
- the fixer 11 a , 12 a may be implemented by one of a band, a belt, and a strap having elasticity.
- the main body 10 may include the IMU 1330 described above.
- the IMU 1330 may be provided outside or inside the housing 11 .
- the IMU 1330 may be provided on the PCB in the housing 11 .
- the IMU 1330 may measure accelerations and angular velocities.
- the mechanisms 20 , 30 , and 40 may include a first structure 20 R, 20 L, a second structure 30 R, 30 L, and a third structure 40 R, 40 L, respectively, as shown in FIGS. 15 through 17 .
- the first structure 20 R, 20 L may assist motions of a thigh and a hip joint of the user during a gait motion.
- the first structure 20 R, 20 L may include a first driver 21 R, 21 L, a first support 22 R, 22 L, and a first fixer 23 R, 23 L.
- the driver 1310 described above may include the first driver 21 R, 21 L, and the description of the driver 1310 provided with reference to FIGS. 13 and 14 may be substituted with the description of the first driver 21 R, 21 L.
- the first driver 21 R, 21 L may be positioned on a hip joint portion of the first structure 20 R, 20 L and generate a rotational force in various magnitudes in a predetermined direction. A torque generated by the first driver 21 R, 21 L may be applied to the first support 22 R, 22 L. The first driver 21 R, 21 L may be set to rotate within a range of motion of a hip joint of a human body.
- the first driver 21 R, 21 L may operate based on the control signal provided by the main body 10 .
- the first driver 21 R, 21 L may be implemented by one of a motor, a vacuum pump, and a hydraulic pump. However, example embodiments are not limited thereto.
- a joint angle sensor may be provided in a vicinity of the first driver 21 R, 21 L.
- the joint angle sensor may detect an angle at which the first driver 21 R, 21 L rotates about a rotation axis.
- the sensor 1320 described above may include the joint angle sensor.
- the first support 22 R, 22 L may be physically connected to the first driver 21 R, 21 L.
- the first support 22 R, 22 L may rotate in a predetermined direction with the rotational force generated by the first driver 21 R, 21 L.
- the first support 22 R, 22 L may be implemented in various shapes.
- the first support 22 R, 22 L may be implemented in a shape of a plurality of segments being connected to each other.
- a joint may be provided between the segments, and the first support 22 R, 22 L may be bent by the joint within a predetermined range.
- the first support 22 R, 22 L may be implemented in a shape of a rod.
- the first support 22 R, 22 L may be implemented by a flexible material so as to be bent within a predetermined range.
- the first fixer 23 R, 23 L may be provided on the first support 22 R, 22 L.
- the first fixer 23 R, 23 L may fix the first support 22 R, 22 L to the thigh of the user.
- FIGS. 15 through 17 illustrate an example in which the first support 22 R, 22 L is fixed to an outer side of the thigh of the user by the first fixer 23 R, 23 L.
- the first support 22 R, 22 L rotates as the first driver 21 R, 21 L operates
- the thigh to which the first support 22 R, 22 L is fixed may also rotate in a direction the same as a direction in which the first support 22 R, 22 L rotates.
- the first fixer 23 R, 23 L may be implemented by one of a band, a belt, and a strap having elasticity, or implemented by a metallic material.
- FIG. 15 illustrates the first fixer 23 R, 23 L implemented by a chain.
- the second structure 30 R, 30 L may assist motions of a lower leg and a knee joint of the user during a gait motion.
- the second structure 30 R, 30 L may include a second driver 31 R, 31 L, a second support 32 R, 32 L, and a second fixer 33 R, 33 L.
- the second driver 31 R, 31 L may be positioned on a knee joint portion of the second structure 30 R, 30 L and generate a rotational force in various magnitudes in a predetermined direction.
- the rotational force generated by the second driver 31 R, 31 L may be applied to the second support 32 R, 32 L.
- the second driver 31 R, 31 L may be set to rotate within a range of motion of a knee joint of a human body.
- the driver 1310 described above may include the second driver 31 R, 31 L.
- the description of the hip joint provided with reference to FIGS. 13 and 14 may similarly apply to the description related to the knee joint.
- the second driver 31 R, 31 L may operate based on the control signal provided by the main body 10 .
- the second driver 31 R, 31 L may be implemented by one of a motor, a vacuum pump, and a hydraulic pump. However, example embodiments are not limited thereto.
- a joint angle sensor may be provided in a vicinity of the second driver 31 R, 31 L.
- the joint angle sensor may detect an angle at which the second driver 31 R, 31 L rotates about a rotation axis.
- the sensor 1320 described above may include the joint angle sensor.
- the second support 32 R, 32 L may be physically connected to the second driver 31 R, 31 L.
- the second support 32 R, 32 L may rotate in a predetermined direction with the rotational force generated by the second driver 31 R, 31 L.
- the second fixer 33 R, 33 L may be provided on the second support 32 R, 32 L.
- the second fixer 33 R, 33 L may fix the second support 32 R, 32 L to the lower leg of the user.
- FIGS. 15 through 17 illustrate an example in which the second support 32 R, 32 L is fixed to an outer side of the lower leg of the user by the second fixer 33 R, 33 L.
- the second support 32 R, 32 L rotates as the second driver 31 R, 31 L operates
- the lower leg to which the second support 32 R, 32 L is fixed may also rotate in a direction the same as a direction in which the second support 32 R, 32 L rotates.
- the second fixer 33 R, 33 L may be implemented by one of a band, a belt, and a strap having elasticity, or implemented by a metallic material.
- the third structure 40 R, 40 L may assist motions of an ankle joint and relevant muscles of the user during a gait motion.
- the third structure 40 R, 40 L may include a third driver 41 R, 41 L, a foot rest 42 R, 42 L, and a third fixer 43 R, 43 L.
- the driver 1310 described above may include the third driver 41 R, 41 L.
- the description of the hip joint provided with reference to FIGS. 13 and 14 may similarly apply to the description related to the ankle joint.
- the third driver 41 R, 41 L may be provided on an ankle joint portion of the third structure 40 R, 40 L and operate based on the control signal provided by the main body 10 .
- the third driver 41 R, 41 L may also be implemented by a motor, similar to the first driver 21 R, 21 L or the second driver 31 R, 31 L.
- a joint angle sensor may be provided in a vicinity of the third driver 41 R, 41 L.
- the joint angle sensor may detect an angle at which the third driver 41 R, 41 L rotates about a rotation axis.
- the sensor 1320 described above may include the joint angle sensor.
- the foot rest 42 R, 42 L may be provided at a position corresponding to a sole of the user and physically connected to the third driver 41 R, 41 L.
- a pressure sensor may be provided on the foot rest 42 R, 42 L to sense a weight of the user.
- a sensing result of the pressure sensor may be used to determine whether the user is wearing the walking assistance apparatus 1 , whether the user is standing, or whether a foot of the user contacts the ground.
- the third fixer 43 R, 43 L may be provided on the foot rest 42 R, 42 L.
- the third fixer 43 R, 43 L may fix the foot of the user to the foot rest 42 R, 42 L.
- the third structure 40 R, 40 L may correspond to the walking assistance apparatus 500 of FIG. 5 .
- the sensor 510 may include the joint angle sensor and the pressure sensor, and the driver 550 may correspond to the third driver 41 R, 41 L.
- the methods according to the above-described example embodiments may be recorded in non-transitory computer-readable media including program instructions to implement various operations of the above-described example embodiments.
- the media may also include, alone or in combination with the program instructions, data files, data structures, and the like.
- the program instructions recorded on the media may be those specially designed and constructed for the purposes of example embodiments, or they may be of the kind well-known and available to those having skill in the computer software arts.
- non-transitory computer-readable media examples include magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM discs, DVDs, and/or Blue-ray discs; magneto-optical media such as optical discs; and hardware devices that are specially configured to store and perform program instructions, such as read-only memory (ROM), random access memory (RAM), flash memory (e.g., USB flash drives, memory cards, memory sticks, etc.), and the like.
- program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter.
- the above-described devices may be configured to act as one or more software modules in order to perform the operations of the above-described example embodiments, or vice versa.
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Abstract
Description
- This application claims under 35 U.S.C. § 119 to Korean Patent Application No. 10-2018-0109919, filed on Sep. 14, 2018, in the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference in their entirety.
- At least one example embodiment relates to a method and/or apparatus for assisting walking of a user. For example, at least some example embodiments relate to a method and/or apparatus for providing an assistance force to assist walking when the user walks.
- With the onset of rapidly aging societies, an increasing number of people may experience inconvenience and/or pain from joint problems. Thus, there may be a growing interest in motion assistance apparatuses enabling the elderly and/or patients having joint problems to walk with less effort. Further, motion assistance apparatuses increasing muscular strength of users for military purposes are being developed.
- Some example embodiments relate to a walking assistance method.
- In some example embodiments, the walking assistance method includes predicting a gait phase of a user within a gait cycle based on information received from a sensor, the information being related to a motion of the user wearing a walking assistance apparatus; and controlling an assistance torque applied to the walking assistance apparatus based on the gait phase.
- In some example embodiments, the method further includes receiving, from the sensor, the information related to the motion of the user wearing the walking assistance apparatus, and wherein the gait phase includes information indicating a gait progress in a period corresponding to the gait cycle.
- In some example embodiments, the predicting includes predicting data indicative of the gait phase by inputting the information into a trained neural network, the data being encoded to have a continuity on a boundary of the gait cycle; and determining the gait phase by decoding the data.
- In some example embodiments, the method further includes encoding the data such that the data includes information indicating a vertex corresponding to the gait phase, among vertices on a circumference of a circle corresponding to the gait cycle, through a trigonometrical function.
- In some example embodiments, the method further includes encoding the data such that the data includes information indicating a vertex corresponding to the gait phase, among a plurality of vertices included in a perimeter of a figure corresponding to the gait cycle.
- In some example embodiments, the controlling includes determining the assistance torque based on the gait phase; and controlling a driver to output the assistance torque.
- In some example embodiments, the determining the assistance torque includes determining a time to apply the assistance torque based on the gait phase; and determining a magnitude of the assistance torque based on the time.
- In some example embodiments, the sensor is on a foot or a shank of the user.
- In some example embodiments, the sensor includes an inertial measurement unit (IMU), and wherein the receiving includes receiving, from the IMU, one or more of acceleration information and rotation velocity information.
- In some example embodiments, the motion of the user includes one or more of a level walking motion, a level running motion, a slope walking motion, and a slope running motion of the user.
- Some example embodiments relate to a method of training a gait phase regression module (PRM).
- In some example embodiments, the method includes receiving, from a first sensor, first information related to a motion of a user wearing a walking assistance apparatus; receiving, from a second sensor, second information related to the motion of the user; determining an initial gait phase within a gait cycle based on the first information and the second information; predicting a gait phase within the gait cycle by applying the first information to the gait PRM; and training the gait PRM based on the gait phase and the initial gait phase.
- In some example embodiments, the gait phase includes information indicating a gait progress in a period corresponding to the gait cycle.
- In some example embodiments, the predicting includes predicting data indicative of the gait phase by inputting the first information into the gait PRM, the data being encoded to have a continuity on a boundary of the gait cycle; and determining the gait phase by decoding the data.
- In some example embodiments, the method further includes encoding the data such that the data includes information indicating a vertex corresponding to the gait phase, among a plurality of vertices included in a perimeter of a figure corresponding to the gait cycle.
- Some example embodiments relate to a non-transitory computer-readable medium including computer readable instructions that, when executed by a computer, cause the computer to perform a walking assistance method.
- Some example embodiments relate to a walking assistance apparatus.
- In some example embodiments, the walking assistance apparatus includes a sensor configured to measure a motion of a user wearing the walking assistance apparatus; a driver configured to assist walking of the user; and at least one processor configured to, predict a gait phase within a gait cycle of the user based on information, and control an assistance torque applied to the walking assistance apparatus based on the gait phase.
- In some example embodiments, the gait phase includes information indicative of a gait progress in a period corresponding to the gait cycle.
- In some example embodiments, the processor is configured to, predict data indicating the gait phase by inputting the information into a trained neural network, the data being encoded to have a continuity on a boundary of the gait cycle, and determine the gait phase by decoding the data.
- In some example embodiments, the processor is configured to encode the data such that the data includes information indicating a vertex corresponding to the gait phase, among a plurality of vertices included in a perimeter of a figure corresponding to the gait cycle.
- In some example embodiments, the sensor includes an inertial measurement unit (IMU), the IMU being configured to measure one or more of acceleration information and rotation velocity information.
- Additional aspects of example embodiments will be set forth in part in the description which follows and, in part, will be apparent from the description, or may be learned by practice of the disclosure.
- These and/or other aspects will become apparent and more readily appreciated from the following description of example embodiments, taken in conjunction with the accompanying drawings of which:
-
FIG. 1 illustrates gait states according to at least one example embodiment; -
FIG. 2 illustrates a transition between gait states according to at least one example embodiment; -
FIG. 3 illustrates a walking assistance apparatus according to at least one example embodiment; -
FIG. 4 illustrates a walking assistance apparatus according to at least one example embodiment; -
FIG. 5 illustrates a configuration of a walking assistance apparatus according to at least one example embodiment; -
FIG. 6 is a flowchart illustrating a walking assistance method according to at least one example embodiment; -
FIG. 7A illustrates graphs to describe forms of gait phases according to at least one example embodiment; -
FIG. 7B illustrates output data of a neural network according to at least one example embodiment; -
FIG. 8 illustrates a walking assistance apparatus and parameters according to at least one example embodiment; -
FIG. 9 is a graph illustrating an assistance torque predetermined based on a gait phase according to at least one example embodiment; -
FIG. 10 is a flowchart illustrating an example of controlling a driver by adjusting a length of a support frame according to at least one example embodiment; -
FIG. 11 illustrates a walking assistance method which distinguishes between two states of stance and swing according to at least one example embodiment; -
FIG. 12 is a block diagram illustrating a walking assistance system according to at least one example embodiment; -
FIGS. 13 and 14 illustrate a hip-type walking assistance apparatus according to at least one example embodiment; and -
FIGS. 15 through 17 illustrate a full body-type walking assistance apparatus according to at least one example embodiment. - The following structural or functional descriptions of example embodiments described herein are merely intended for the purpose of describing the example embodiments described herein and may be implemented in various forms. However, it should be understood that these example embodiments are not construed as limited to the illustrated forms.
- Various alterations and modifications may be made to the examples. Here, the examples are not construed as limited to the disclosure and should be understood to include all changes, equivalents, and replacements within the idea and the technical scope of the disclosure.
- Terms such as first, second, A, B, (a), (b), and the like may be used herein to describe components. Each of these terminologies is not used to define an essence, order or sequence of a corresponding component but used merely to distinguish the corresponding component from other component(s). For example, a first component may be referred to a second component, and similarly the second component may also be referred to as the first component.
- It should be noted that if it is described in the specification that one component is “connected,” “coupled,” or “joined” to another component, a third component may be “connected,” “coupled,” and “joined” between the first and second components, although the first component may be directly connected, coupled or joined to the second component. In addition, it should be noted that if it is described in the specification that one component is “directly connected” or “directly joined” to another component, a third component may not be present therebetween. Likewise, expressions, for example, “between” and “immediately between” and “adjacent to” and “immediately adjacent to” may also be construed as described in the foregoing.
- The terminology used herein is for the purpose of describing particular examples only, and is not to be used to limit the disclosure. As used herein, the terms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used herein, the terms “include, “comprise,” and “have” specify the presence of stated features, numbers, operations, elements, components, and/or combinations thereof, but do not preclude the presence or addition of one or more other features, numbers, operations, elements, components, and/or combinations thereof.
- Unless otherwise defined, all terms, including technical and scientific terms, used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Terms, such as those defined in commonly used dictionaries, are to be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art, and are not to be interpreted in an idealized or overly formal sense unless expressly so defined herein.
-
FIG. 1 illustrates gait states according to at least one example embodiment. - Gait phases of a leg of a user with respect to a gait may be predefined.
- Referring to
FIG. 1 , gait phases may include discontinuous values classified into a stance and a swing. Gait phases of a left leg may include a left stance LSt and a left swing LSw. Gait phases of a right leg may include a right stance RSt and a right swing RSw. - The gait phases may have continuous values with respect to a gait progress from a predetermined start point to a predetermined end point. For example, a gait phase with respect to a stance may be a gait phase based on a stance. In detail, a
gait phase 0% with respect to the stance may be mapped to a point in time at which the stance is started, agait phase 60% with respect to the stance may be mapped to a point in time at which a swing is started, and agait phase 100% with respect to the stance may be mapped to a point in time immediately before the stance is started. Similarly, a gait phase with respect to a swing may be a gait phase based on a swing. In detail, agait phase 0% with respect to the swing may be mapped to a point in time at which the swing is started, agait phase 60% with respect to the swing may be mapped to a point in time at which a stance is started, and agait phase 100% with respect to the swing may be mapped to a point in time immediately before the swing is started. In another example, the gait phases may be continuous values with respect to a gait progress with a start point of “0” and an end point of “1”. - The stance and the swing may further be divided into a plurality of phases. For example, the stance may further be divided into an initial contact, a weight bearing, a middle stance, a terminal stance, and a pre-swing. The swing may further be divided into an initial swing, a middle swing, and a terminal swing. The stance and the swing may further be divided differently depending on an example embodiment, and are not limited thereto.
-
FIG. 2 illustrates a transition between gait states according to at least one example embodiment. - According to a general gait mechanism, gait phases of a leg may include a stance and a swing, and the stance and the swing may be performed alternately for a gait.
- Referring to
FIG. 2 , aright gait state 210 with respect to amotion 200 of a right leg in response to a gait may include a right stance and a right swing. A stance may include a weight bearing, a middle stance, and a terminal stance, but are not limited to the disclosed and illustrated embodiments. With respect to themotion 200 of the right leg, aleft gait state 220 with respect to a motion (not shown) of a left leg may include a left stance and a left swing. - A normal transition between gait states may differ depending on a gait state at a time of starting a gait. Based on an order in which events indicating starts of gait states occur, the gait states may transition in an order of a right stance, a left swing, a left stance, and a right swing. After the right swing is performed, the right stance may be performed again.
- A user may feel discomfort in walking when a muscular strength of an ankle of the user is weakened due to aging or disease. For example, an end portion of a foot needs to be lifted when a leg starts swinging. If not, the swinging leg may be bumped into a floor. That is, an angle of the ankle should be adjusted as a gait phase proceeds or changes. A walking assistance apparatus may be provided to a user who has a difficulty in adjusting an angle of an ankle by himself or herself due to a weakened muscular strength of the ankle. The walking assistance apparatus may be worn around the ankle of the user, determine a gait phase of the user, and output an assistance torque corresponding to the determined gait phase. The assistance torque may be used to adjust the angle of the ankle of the user.
- Further, a future gait phase as well as a gait phase of a current point in time may need to be predicted. For example, there may occur a case in which the walking assistance apparatus needs to output an assistance torque at a point in time earlier than a point in time of switching stance/swing.
- Hereinafter, a method of assisting walking of a user by providing an assistance torque to an ankle of the user will be described with reference to
FIGS. 3 through 17 . -
FIG. 3 illustrates a walking assistance apparatus according to at least one example embodiment. - Referring to
FIG. 3 , a walkingassistance apparatus 300 may include asole frame 310, alower fastener 320, anupper fastener 330, afirst support frame 340, asecond support frame 350, and an inertial measurement unit (IMU) 360. The walkingassistance apparatus 300 may predict a gait phase only using theIMU 360, without using other types of sensors. For example, the walkingassistance apparatus 300 may predict the gait phase based on data obtained only using theIMU 360, without using a pressure sensor disposed on a sole. - The
IMU 360 may include accelerometers and gyroscopes. TheIMU 360 may sense accelerations in a three-dimensional (3D) space using the accelerometers. TheIMU 360 may sense rotation velocities in the 3D space using the gyroscopes. TheIMU 360 may be positioned at a foot or a shank of a user. - The
first support frame 340 may connect thelower fastener 320 and theupper fastener 330. Thelower fastener 320 may be connected to thesole frame 310. Thesecond support frame 350 may connect thesole frame 310 and theupper fastener 330. Theupper fastener 330 may be worn on a calf or the shank of the user. - A length of the
first support frame 340 and a length of thesecond support frame 350 may be adjusted. For example, the length of thefirst support frame 340 and the length of thesecond support frame 350 may be adjusted by a driver (not shown). The driver may adjust the length of thefirst support frame 340 and the length of thesecond support frame 350 using a mechanism. - When the length of the
first support frame 340 decreases and the length of thesecond support frame 350 increases, the ankle of the user may be lifted. Conversely, when the length of thefirst support frame 340 increases and the length of thesecond support frame 350 decreases, the ankle of the user may extend. - Although
FIG. 3 illustrates the walkingassistance apparatus 300 including thefirst support frame 340 and thesecond support frame 350, the number of support frames is not limited thereto. For example, the walkingassistance apparatus 300 may include only thefirst support frame 340, or may include at least three support frames. -
FIG. 4 illustrates a walking assistance apparatus according to at least one example embodiment. - Referring to
FIG. 4 , a walkingassistance apparatus 400 may include asole frame 410, alower fastener 420, anupper fastener 430, amotor 440, and anIMU 450. - The
IMU 450 may include accelerometers and gyroscopes. TheIMU 450 may sense accelerations in a 3D space using the accelerometers. TheIMU 450 may sense rotation velocities in the 3D space using the gyroscopes. TheIMU 450 may be positioned at a foot or a shank of a user. - The
motor 440 may be connected to thelower fastener 420 and theupper fastener 430. A driver (not shown) may control themotor 440 to output a torque. When themotor 440 outputs a torque, an angle between thelower fastener 420 and theupper fastener 430 may be adjusted. For example, when the angle between thelower fastener 420 and theupper fastener 430 decreases, an ankle of the user may be lifted. Conversely, when the angle between thelower fastener 420 and theupper fastener 430 increases, the ankle of the user may extend. - Hereinafter, the method of assisting walking of a user will be described in detail with reference to
FIGS. 5 through 17 . -
FIG. 5 illustrates a configuration of a walking assistance apparatus according to at least one example embodiment. - A walking
assistance apparatus 500 may include at least onesensor 510, aprocessor 530, and adriver 550. The walkingassistance apparatus 500 may further include acommunicator 520, and amemory 540. The walkingassistance apparatus 500 may correspond to thewalking assistance apparatus 300 ofFIG. 3 and the walkingassistance apparatus 400 ofFIG. 4 such that the walkingassistance apparatus 500 also include the structural elements (e.g., fasteners, frames or motor) illustrated inFIGS. 3 orFIG. 4 . The walkingassistance apparatus 500 may be an ankle exoskeleton device. - The at least one
sensor 510 may include an IMU. The IMU may measure accelerations and rotation velocities generated in response to a motion of the IMU. For example, the IMU may measure X-axial, Y-axial, and Z-axial accelerations and X-axial, Y-axial, and Z-axial angular velocities corresponding to a gait motion of a user. - The
communicator 520 may be connected to thesensor 510, theprocessor 530, and thememory 540, and transmit and receive data to and from thesensor 510, theprocessor 530, and thememory 540. Thecommunicator 520 may be connected to an external device and transmit and receive data to and from the external device. Hereinafter, to transmit and receive “A” may be to transmit and receive “information or data indicating A”. - The
communicator 520 may be implemented by a circuitry in thewalking assistance apparatus 500. For example, thecommunicator 520 may include an internal bus and an external bus. In another example, thecommunicator 520 may be an element which connects the walkingassistance apparatus 500 and the external device. Thecommunicator 520 may be an interface. Thecommunicator 520 may receive data from the external device and transmit the data to theprocessor 530 and thememory 540. - The
processor 530 may process the data received by thecommunicator 520 and data stored in thememory 540. The “processor” may be a data processing device implemented by hardware including a circuit having a physical structure to perform desired operations. For example, the desired operations may include instructions or codes included in a program. For example, the hardware-implemented data processing device may include a microprocessor, a central processing unit (CPU), a processor core, a multi-core processor, a multiprocessor, an application-specific integrated circuit (ASIC), and a field programmable gate array (FPGA). - The
processor 530 may execute computer-readable codes (for example, software) stored in a memory, for example, thememory 540, and instructions triggered by theprocessor 530. - For example, the
processor 530 may execute the computer-readable codes such that theprocessor 530 is transformed into a special purpose processor to perform the walking assistance method ofFIG. 6 and/or train a gait phase regression module (PRM) based on a predicted gait phase and a determined gait phase. Therefore, theprocessor 530 may improve the functioning of the walking assistance apparatus itself by increasing the accuracy of predicting continuous motions of the gait associated with a boundary of a gait cycle. - The
memory 540 may store the data received by thecommunicator 520 and the data processed by theprocessor 530. For example, thememory 540 may store the program. The stored program may be a set of syntaxes coded to assist walking of a user and executable by theprocessor 530. - The
memory 540 may include at least one volatile memory, non-volatile memory, random access memory (RAM), flash memory, hard disk drive, and optical disk drive. - The
memory 540 may store a command set, for example, software, to operate thewalking assistance apparatus 500. The command set to operate thewalking assistance apparatus 500 may be executed by theprocessor 530. - The
driver 550 may include mechanisms to adjust an angle of an ankle of the user. For example, thedriver 550 may include a motor, and a torque output by the motor may be used to adjust the angle of the ankle. In another example, thedriver 550 may include a power converting device which adjusts a length of a support frame. The power converting device may convert a rotational motion generated by thedriver 550 into a rectilinear motion. - The walking
assistance apparatus 500 may include a powered orthosis or robotic orthosis which assists a physiological function like an ankle exoskeleton device, and a robotic prosthesis which replaces a missing body part. - The
sensor 510, thecommunicator 520, theprocessor 530, thememory 540, and thedriver 550 will be described further with reference toFIGS. 6 through 17 . -
FIG. 6 is a flowchart illustrating a walking assistance method according to at least one example embodiment. -
Operations 610 through 630 may be performed by the walkingassistance apparatus 500 ofFIG. 5 . The walkingassistance apparatus 500 may be implemented by one or more hardware modules, one or more software modules, or various combinations thereof. - In
operation 610, the walkingassistance apparatus 500 may receive or obtain information related to a motion of a user from a sensor. For example, thesensor 510 may include an IMU. The IMU may measure acceleration information, rotation velocity information, or a combination thereof in relation to a motion of a user. The rotation velocity information may be information related to an angular velocity. - In
operation 620, the walkingassistance apparatus 500 may predict a gait phase of the user based on the received information related to the motion of the user. The gait phase may include information indicating a gait process in a predefined period corresponding to a gait cycle. The gait cycle may be a period including repetitive patterns occurring during walking and include, for example, the right stance RSt and the right swing RSw ofFIG. 1 . The gait cycle may be defined separately for a right leg and a left leg. Referring toFIG. 2 , a gait cycle for the right leg and a gait cycle for the left leg may differ from each other. - The predefined period may be a period defined repeatedly for a gait, and may include, for example, a period from a start point of a stance period to a start point of a subsequent stance period, or a period from a start point of a swing period to a start point of a subsequent swing period. In another example, the predefined period may include a period from a predetermined point in time instead of a start point to a corresponding point in time of a subsequent cycle.
- For example, the walking
assistance apparatus 500 may predict a gait progress in a gait cycle based on motion information measured by the sensor. An example of predicting a gait phase of a user will be described in detail with reference toFIGS. 7A through 8 . - In
operation 630, the walkingassistance apparatus 500 may control an assistance torque based on the predicted gait phase. For example, the walkingassistance apparatus 500 may calculate an assistance torque corresponding to the gait progress. A trajectory of the assistance torque with respect to the gait phase may be preset corresponding to the gait progress. The walkingassistance apparatus 500 may determine a point in time at which the assistance torque is to be applied based on the predicted gait phase, and determine a magnitude of the assistance torque corresponding to the determined point in time. - The walking
assistance apparatus 500 may further performoperation 640. Inoperation 640, the walkingassistance apparatus 500 may control thedriver 550 to output the assistance torque. An example of controlling the driver will be described further with reference toFIG. 10 . -
FIG. 7A illustrates graphs to describe forms of gait phases according to at least one example embodiment. - Referring to
FIG. 7A , a gait phase related to motion information of a user may be represented separately based on a stance and a swing. For example, agraph graph 710 related to a gait phase with respect to a stance of a leg, a stance state may be represented with a value of “1”, and a swing state may be represented with a value of “0”. In thegraph 720 related to a gait phase with respect to a swing of a leg, a swing state may be represented with a value of “1”, and a stance state may be represented with a value of “0”. - The gait phase may be represented with continuous data related to a gait progress in a predefined period corresponding to a gait cycle. For example, referring to a
graph 730, the gait phase may be mapped to “0” at a first point in time at which a stance is started, and the gait phase may be mapped to “1” at a second point in time at which the stance is restarted. The gait phase between the first point in time and the second point in time may be mapped to continuous values between “0” and “1” related to the gait progress. Referring to agraph 740, the gait phase with respect to the swing may be mapped in a manner substantially the same as the manner described through thegraph 730. - The
graph graph graph graph graph graph - The walking
assistance apparatus 500 may predict continuous gait phases, for example, thegraph - The walking
assistance apparatus 500 may use a gait phase regression module (PRM) to predict the continuous gait phases corresponding to the information related to the motion of the user. The gait PRM may include a pre-trained neural network. In this example, the walkingassistance apparatus 500 may input the information related to the motion of the user into the pre-trained neural network. The neural network may include an input layer, a hidden layer, and an output layer. The input layer may have a structure suitable for receiving the information related to the motion of the user collected by the sensor, and the output layer may have a structure suitable for outputting data indicating the continuous gait phases. - An operation of training a neural network with respect to a gait phase may be performed by the walking
assistance apparatus 500. For example, the walkingassistance apparatus 500 may receive, from a first sensor, first information related to a motion of a user wearing the walking assistance apparatus. The first sensor may include an IMU. The walkingassistance apparatus 500 may receive, from a second sensor, second information related to the motion of the user. The second sensor may include a contact sensor. The contact sensor may sense whether a foot of the user contacts the ground. The walkingassistance apparatus 500 may determine a gait phase corresponding to the first information based on the second information. The determined gait phase may be utilized as a label or a ground truth to train the neural network. The walkingassistance apparatus 500 may predict the gait phase by applying the first information to the gait PRM. The walkingassistance apparatus 500 may train the gait PRM based on the predicted gait phase and the determined gait phase. For example, the walkingassistance apparatus 500 may train parameters of the neural network so as to minimize a difference between the predicted gait phase and the determined gait phase. - An operation of training a neural network with respect to a gait phase may be performed by a separate server device. The server device may use training data provided in advance or training data collected from at least one user. The server device may also use training data generated by a simulation.
- The neural network may be pre-trained to predict a gait phase corresponding to acceleration information and rotation velocity information according to various gait patterns. For example, the neural network may be pre-trained with a gait phase corresponding to acceleration information and rotation velocity information according to various gait patterns such as level walking, level running, slope walking, and slop running of a normal user. Further, the neural network may be pre-trained with a gait phase corresponding to acceleration information and rotation velocity information according to various gait patterns such as level walking, level running, slope walking, and slop running of a user having a difficulty in normal walking.
- The neural network may be trained to predict gait phases, for example, the
graph graph 750, rather than thegraph graph graph 750, a precise control may be relatively difficult when compared to an example of an unsmoothed graph like thegraph graph -
FIG. 7B illustrates output data of a neural network according to at least one example embodiment. Referring toFIG. 7B , agait phase 760 having continuous values in a gait cycle may be encoded into acircle 770 having a continuity on a boundary of the gait cycle to train a neural network. For example, afirst gait phase 761 belonging to a second half of the gait cycle may be mapped to avertex 771 on thecircle 770, asecond gait phase 762 belonging to the boundary of the gait cycle may be mapped to avertex 772 on thecircle 770, and athird gait phase 763 belonging to a first half of a subsequent gait cycle may be mapped to avertex 773 on thecircle 770. - When six-dimensional (6D) information related to measured accelerations and angular velocities is input, the neural network may be pre-trained to output a two-dimensional (2D) vector of cosine and sine values indicating a gait phase with respect to a stance, a 2D vector of cosine and sine values indicating a gait phase with respect to a swing, or a four-dimensional (4D) vector combining the same. The 2D vector may correspond to coordinates indicating a vertex on the
circle 770. - For example, the neural network may be trained to output y1=cos0 and y2=sin0 based on 6D information related to accelerations and angular velocities measured at a point in time at which a stance is started. The neural network may be trained to output y1=cos2π and y2=sin2π based on 6D information related to accelerations and angular velocities measured at a point in time immediately before the stance is started. The neural network may be trained to output y1=cos1.2π and y2=sin1.2π based on 6D information related to accelerations and angular velocities measured at a point in time at which a swing is started.
- The neural network may be trained to output data encoded to have a continuity on the boundary of the gait cycle, rather than being trained to output the
gait phase 760 directly. Thus, a result predicted by the neural network may be inhibited (or, alternatively, prevented) from being smoothed on the boundary of the gait cycle. - In further detail, in an example in which the neural network is trained to map the measured accelerations and angular velocities directly to a gait phase, a smoothed gait phase may be predicted on the boundary of the gait cycle since a gait phase at a point in time immediately before a stance is started and a gait phase at a point in time at which the stance is started are discontinuous.
- Conversely, in an example in which the neural network is trained such that the measured accelerations and angular velocities correspond to trigonometrical function data corresponding to the gait phase, a value output at a point in time immediately before the stance is started and a value output at a point in time at which the stance is started are maintained to be continuous by a periodicity of a trigonometrical function, whereby prediction of a smoothed gait phase on the boundary of the gait cycle may be inhibited (or, alternatively, prevented).
- In an inference process after training is completed, when the encoded data is output from the neural network, the walking
assistance apparatus 500 may obtain the gait phase by decoding the corresponding data. Operations of the inference process will be described later with reference toFIGS. 8 through 11 . - Although not shown in the drawings, another figure may be used for encoding, instead of a circle. For example, the other figure may include a polygon, and an untwisted closed curve. In this example, the data may include information indicating a vertex corresponding to a gait phase, among a plurality of vertices included in a perimeter of a figure corresponding to a gait cycle. Further, the data may have a continuity on the boundary of the gait cycle. For example, on a boundary between a current gait cycle to a subsequent gait cycle, a figure, for example, a polygon, may be determined such that data may change by a minimum unit representing a gait phase.
-
FIG. 8 illustrates a walking assistance apparatus and parameters according to at least one example embodiment. - Referring to
FIG. 8 , anIMU 810 may be attached to a foot or a shank of a user to measure a motion of the user and obtain 6D information related to accelerations and angular velocities. For example, theIMU 810 may measure X-axial, Y-axial, and Z-axial accelerations, and X-axial, Y-axial, and Z-axial angular velocities corresponding to a gait motion of the user. - The 6D information related to the measured accelerations and angular velocities may be input into a gait phase regression module (PRM) 820. The
gait PRM 820 may be a module trained by machine learning, and may output a trigonometrical function value of a gait phase with respect to a stance and a gait phase with respect to a swing. Trigonometrical function data with respect to the gait phase may be referred to as first data. The machine learning may include a recurrent neural network (RNN). The trigonometrical function may include a cosine function and a sine function. For example, thegait PRM 820 may output cosine values and sine values of the gait phase with respect to the stance and the gait phase with respect to the swing as a 4D vector. - The
gait PRM 820 may determine the gait phase with respect to the stance and the gait phase with respect to the swing using the 4D vector corresponding to the cosine values and the sine values of the gait phase with respect to the stance and the gait phase with respect to the swing. For example, thegait PRM 820 may obtain the gait phase with respect to the stance and the gait phase with respect to the swing by performing an arctan operation on the determined cosine and sine values. The gait phase obtained by thegait PRM 820 may be an unsmoothed graph like thegraph - A
controller 830 may control adriver 840 to output an assistance force to assist walking of a user. A predetermined assistance torque may be controlled based on a gait phase at a desired point in time. For example, thecontroller 830 may output a control signal to control thedriver 840 to generate the torque. Thecontroller 830 may include a communicator, a processor, and a memory. - The
driver 840 may generate the torque based on the control signal output from thecontroller 830. Thedriver 840 may provide a driving force to an ankle of the user. For example, thedriver 840 may control the ankle such that a center of gravity of the user may be formed at a front side of a sole. Thedriver 840 may include a motor which generates a rotational torque. -
FIG. 9 is a graph illustrating an assistance torque predetermined based on a gait phase according to at least one example embodiment. - Referring to a
graph 900 ofFIG. 9 , an assistance torque needed by a user may be predetermined based on a gait phase. The walkingassistance apparatus 500 may calculate an assistance torque corresponding to a stance or a swing. A point in time at which the assistance torque is to be applied and a magnitude of the assistance torque corresponding to the point in time may be determined based on the predicted gait phase. A trajectory of the assistance torque with respect to the gait phase may be preset. For example, the trajectory of the assistance torque may be preset as shown in agraph 900. In thegraph 900, an axis x may denote the point in time at which the assistance torque is to be applied, and an axis y may denote the magnitude of the assistance torque corresponding to the point in time. An angle of the ankle of the user may be adjusted by the assistance torque. The walkingassistance apparatus 500 may control a driver to output an assistance torque. An example of controlling the driver will be described in detail with reference toFIG. 10 . -
FIG. 10 is a flowchart illustrating an example of controlling a driver by adjusting a length of a support frame according to at least one example embodiment. -
Operation 640 ofFIG. 6 may includeoperations embodiment using operations walking assistance apparatus 300 ofFIG. 3 . - In
operation 1010, the walkingassistance apparatus 500 may calculate a length of a support frame corresponding to the determined assistance torque. For example, the length of the support frame corresponding to the assistance torque may be stored in advance. - In
operation 1020, the walkingassistance apparatus 500 may control thedriver 550 such that the support frame may have the calculated length. For example, thedriver 550 may adjust the length of the support frame using a power converting device. The power converting device may be a device which converts a rotational motion of a motor into a rectilinear motion. -
FIG. 11 illustrates a walking assistance method which distinguishes between two states of stance and swing according to at least one example embodiment. - Referring to
FIG. 11 , when 6D information related to measured accelerations and angular velocities is input into a classifier provided in advance, the classifier may output a probability of a stance P_stance and a probability of a swing P_swing. - A walking assistance apparatus may switch an inner state as expressed by
Equation 1. -
If (current state==Stance AND P_swing>alpha) Then current state←Swing -
If (current state==Swing AND P_stance>alpha) Then current state←Stance [Equation 1] - In
Equation 1, alpha denotes a predetermined threshold, and may be determined to be, for example, a value between “0” and “1”. As alpha is relatively close to “1”, the stance/swing states may switch conservatively. In an example in which alpha=0.9 and the inner state is the stance state, the inner state may switch from the stance state to the swing state only when the probability of the swing output from the classifier is greater than or equal to 90%. -
FIG. 12 is a block diagram illustrating a walking assistance system according to at least one example embodiment. - Referring to
FIG. 12 , awalking assistance system 1200, also referred to as a gait assist system or a walking assist system, may include the walkingassistance apparatus 500, and aremote controller 1210. - The
remote controller 1210 may control an overall operation of the walkingassistance apparatus 500 in response to a user input. For example, theremote controller 1210 may initiate or terminate the operation of the walkingassistance apparatus 500. Further, theremote controller 1210 may control an assistance torque output from the walkingassistance apparatus 500 to control a walking assistance for the user. - The
remote controller 1210 may include adisplay 1230. Thedisplay 1230 may be implemented by a touch screen, a liquid crystal display (LCD), a thin film transistor-LCD (TFT-LCD), a light emitting diode (LED) display, an organic LED (OLED) display, an active matrix OLED (AMOLED) display, or a flexible display. - The
remote controller 1210 may provide a user with a menu and/or a user interface (UI) corresponding to a function to control the walkingassistance apparatus 500 through thedisplay 1230. - The
display 1230 may display an operating state of the walkingassistance apparatus 500 to the user based on the control of theremote controller 1210. - Hereinafter, a hip-type walking assistance apparatus which may be additionally combined with the walking
assistance apparatus 500 described with reference toFIGS. 5 through 12 will be described with reference toFIGS. 13 and 14 . The hip-type walking assistance apparatus may be an apparatus which provides a walking assistance force to a hip joint of a user. The walkingassistance apparatus 500 may be connected to the hip-type walking assistance apparatus via wired communication or wireless communication. The walkingassistance apparatus 500 and the hip-type walking assistance apparatus may provide the user with an assistance torque with respect to a gait phase determined for a motion of the user. For example, the walkingassistance apparatus 500 may provide the assistance torque to an ankle joint of the user, and the hip-type walking assistance apparatus may provide the assistance torque to the hip joint of the user. -
FIGS. 13 and 14 illustrate a hip-type walking assistance apparatus according to at least one example embodiment. - Referring to
FIG. 13 , a hip-typewalking assistance apparatus 1300 may be attached to a user and assist walking of the user. The walkingassistance apparatus 1300 may be a wearable device. - Example embodiments described with reference to
FIGS. 13 and 14 may be applied to the hip-type walking assistance apparatus. However, the example embodiments are not limited thereto, and may be applied to any device which assists walking of a user. - The hip-type
walking assistance apparatus 1300 may include adriver 1310, asensor 1320, anIMU 1330, and acontroller 1340. - The
driver 1310 may provide a driving force to a hip joint of the user. For example, thedriver 1310 may be positioned on a right hip and/or a left hip of the user. Thedriver 1310 may include a motor which generates a rotational torque. - The
sensor 1320 may measure an angle of the hip joint of the user during walking. Information related to the angle of the hip joint sensed by thesensor 1320 may include an angle of a right hip joint, an angle of a left hip joint, a difference between the angles of both hip joints, and movement directions of the hip joints. For example, thesensor 1320 may be positioned in thedriver 1310. - The
sensor 1320 may include a potentiometer. The potentiometer may sense right (R)-axial and left (L)-axial joint angles and R-axial and L-axial joint angular velocities corresponding to a gait motion of the user. - The
IMU 1330 may measure acceleration information and pose information during walking. For example, theIMU 1330 may sense X-axial, Y-axial, and Z-axial accelerations and X-axial, Y-axial, and Z-axial angular velocities corresponding to the gait motion of the user. - The hip-type
walking assistance apparatus 1300 may detect a point at which a foot of the user lands based on the acceleration information measured by theIMU 1330. - The hip-type
walking assistance apparatus 1300 may include other sensors which sense a change in biosignal or quantity of motion of the user in response to the gait motion, in addition to thesensor 1320 and theIMU 1330 described above. The other sensors may include, for example, an electromyogram (EMG) sensor and an electroencephalogram (EEG) sensor. - The
controller 1340 may control thedriver 1310 to output an assistance force to assist walking of the user. For example, the hip-typewalking assistance apparatus 1300 may include twodrivers 1310 for the left hip and the right hip. Thecontroller 1340 may output a control signal to control thedriver 1310 to generate a torque. Thecontroller 1340 may include a communicator, a processor, and a memory. - The
driver 1310 may generate the torque based on the control signal output from thecontroller 1340. The hip-typewalking assistance apparatus 1300 may include thedriver 1310 for the right leg and thedriver 1310 for the left leg. For example, thecontroller 1340 may be designed to control one of thedrivers 1310. In an example in which thecontroller 1340 controls only one of thedrivers 1310, there may be a plurality ofcontrollers 1340. In another example, thecontroller 1340 may be designed to control all thedrivers 1310. - Unlike the hip-type
walking assistance apparatus 1300 described with reference toFIGS. 13 and 14 , the walkingassistance apparatus 500 may be included in a full body-typewalking assistance apparatus 1 which will be described with reference toFIGS. 15 through 17 . The full body-typewalking assistance apparatus 1 may be a device which provides a walking assistance force to each of a hip joint, a knee joint, and an ankle joint of a user. -
FIGS. 15 through 17 illustrate a full body-type walking assistance apparatus according to at least one example embodiment.FIG. 15 is a front view of the full body-typewalking assistance apparatus 1,FIG. 16 is a side view of the full body-typewalking assistance apparatus 1, andFIG. 17 is a rear view of the full body-typewalking assistance apparatus 1. - The full body-type
walking assistance apparatus 1 may include thedriver 1310, thesensor 1320, theIMU 1330, and thecontroller 1340 described above. - As shown in
FIGS. 15 through 17 , the full body-typewalking assistance apparatus 1 may have an exoskeleton structure such that the full body-typewalking assistance apparatus 1 may be worn on a left leg and a right leg of a user. The user may perform motions such as an extension, a flexion, an adduction, and an abduction while wearing the walkingassistance apparatus 1. The extension may be a motion to extend a joint, and the flexion may be a motion to flex the joint. The adduction may be a motion to move a leg close to a central axis of a body. The abduction may be a motion to stretch the leg away from the central axis of the body. - Referring to
FIGS. 14 through 17 , the full body-typewalking assistance apparatus 1 may include amain body 10, and mechanisms 20, 30, and 40. - The
main body 10 may include ahousing 11. Various components may be provided in thehousing 11. The components provided in thehousing 11 may include, for example, a CPU, a printed circuit board (PCB), various types of storage devices, and a power source. Themain body 10 may include thecontroller 1340 described above. Thecontroller 1340 may include a CPU and a PCB. - The CPU may be a microprocessor. The microprocessor may include an arithmetic logical operator, a register, a program counter, a command decoder, and/or a control circuit on a silicon chip. The CPU may select a control mode suitable for a gait environment and generate a control signal to control operations of the mechanisms 20, 30, and 40 based on the selected control mode.
- The PCB may be a board on which a predetermined circuit is printed. A CPU and/or various storage devices may be provided on the PCB. The PCB may be fixed to an inner side surface of the
housing 11. - Various types of storage devices may be provided in the
housing 11. The storage devices may be magnetic disk storage devices to store data by magnetizing a surface of a magnetic disk, and semiconductor memory devices to store data using various types of memory semiconductors. - The power source provided in the
housing 11 may supply a driving power to the various components provided in thehousing 11, or the mechanisms 20, 30, and 40. - The
main body 10 may further include awaist support 12 to support a waist of the user. Thewaist support 12 may have a shape of a curved plane so as to support the waist of the user. - The
main body 10 may further include afixer 11 a to fix thehousing 11 to a hip of the user, and afixer 12 a to fix thewaist support 12 to the waist of the user. Thefixer - The
main body 10 may include theIMU 1330 described above. For example, theIMU 1330 may be provided outside or inside thehousing 11. TheIMU 1330 may be provided on the PCB in thehousing 11. TheIMU 1330 may measure accelerations and angular velocities. - The mechanisms 20, 30, and 40 may include a
first structure second structure third structure FIGS. 15 through 17 . - The
first structure first structure first driver first support first fixer - The
driver 1310 described above may include thefirst driver driver 1310 provided with reference toFIGS. 13 and 14 may be substituted with the description of thefirst driver - The
first driver first structure first driver first support first driver - The
first driver main body 10. Thefirst driver - A joint angle sensor may be provided in a vicinity of the
first driver first driver sensor 1320 described above may include the joint angle sensor. - The
first support first driver first support first driver - The
first support first support first support first support first support - The
first fixer first support first fixer first support -
FIGS. 15 through 17 illustrate an example in which thefirst support first fixer first support first driver first support first support - The
first fixer FIG. 15 illustrates thefirst fixer - The
second structure second structure second driver second support second fixer - The
second driver second structure second driver second support second driver - The
driver 1310 described above may include thesecond driver FIGS. 13 and 14 may similarly apply to the description related to the knee joint. - The
second driver main body 10. Thesecond driver - A joint angle sensor may be provided in a vicinity of the
second driver second driver sensor 1320 described above may include the joint angle sensor. - The
second support second driver second support second driver - The
second fixer second support second fixer second support FIGS. 15 through 17 illustrate an example in which thesecond support second fixer second support second driver second support second support - The
second fixer - The
third structure third structure third driver 41R, 41L, afoot rest third fixer - The
driver 1310 described above may include thethird driver 41R, 41L. The description of the hip joint provided with reference toFIGS. 13 and 14 may similarly apply to the description related to the ankle joint. - The
third driver 41R, 41L may be provided on an ankle joint portion of thethird structure main body 10. Thethird driver 41R, 41L may also be implemented by a motor, similar to thefirst driver second driver - A joint angle sensor may be provided in a vicinity of the
third driver 41R, 41L. The joint angle sensor may detect an angle at which thethird driver 41R, 41L rotates about a rotation axis. Thesensor 1320 described above may include the joint angle sensor. - The
foot rest third driver 41R, 41L. - A pressure sensor may be provided on the
foot rest assistance apparatus 1, whether the user is standing, or whether a foot of the user contacts the ground. - The
third fixer foot rest third fixer foot rest - In an example, the
third structure walking assistance apparatus 500 ofFIG. 5 . For example, thesensor 510 may include the joint angle sensor and the pressure sensor, and thedriver 550 may correspond to thethird driver 41R, 41L. - The methods according to the above-described example embodiments may be recorded in non-transitory computer-readable media including program instructions to implement various operations of the above-described example embodiments. The media may also include, alone or in combination with the program instructions, data files, data structures, and the like. The program instructions recorded on the media may be those specially designed and constructed for the purposes of example embodiments, or they may be of the kind well-known and available to those having skill in the computer software arts. Examples of non-transitory computer-readable media include magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM discs, DVDs, and/or Blue-ray discs; magneto-optical media such as optical discs; and hardware devices that are specially configured to store and perform program instructions, such as read-only memory (ROM), random access memory (RAM), flash memory (e.g., USB flash drives, memory cards, memory sticks, etc.), and the like. Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter. The above-described devices may be configured to act as one or more software modules in order to perform the operations of the above-described example embodiments, or vice versa.
- A number of example embodiments have been described above. Nevertheless, it should be understood that various modifications may be made to these example embodiments. For example, suitable results may be achieved if the described techniques are performed in a different order and/or if components in a described system, architecture, device, or circuit are combined in a different manner and/or replaced or supplemented by other components or their equivalents. Accordingly, other implementations are within the scope of the following claims.
Claims (20)
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